{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单发多框检测（SSD）\n",
    "\n",
    "我们在前几节分别介绍了边界框、锚框、多尺度目标检测和数据集，下面我们基于这些背景知识来构造一个目标检测模型：单发多框检测（single shot multibox detection，SSD）[1]。它简单、快速，并得到了广泛应用。该模型的一些设计思想和实现细节常适用于其他目标检测模型。\n",
    "\n",
    "\n",
    "## 模型\n",
    "\n",
    "图9.4描述了单发多框检测模型的设计。它主要由一个基础网络块和若干个多尺度特征块串联而成。其中基础网络块用来从原始图像中抽取特征，因此一般会选择常用的深度卷积神经网络。单发多框检测论文中选用了在分类层之前截断的VGG [1]，现在也常用ResNet替代。我们可以设计基础网络，使它输出的高和宽较大。这样一来，基于该特征图生成的锚框数量较多，可以用来检测尺寸较小的目标。接下来的每个多尺度特征块将上一层提供的特征图的高和宽缩小（如减半），并使特征图中每个单元在输入图像上的感受野变得更广阔。如此一来，图9.4中越靠近顶部的多尺度特征块输出的特征图越小，故而基于特征图生成的锚框也越少，加之特征图中每个单元感受野越大，因此更适合检测尺寸较大的目标。由于单发多框检测基于基础网络块和各个多尺度特征块生成不同数量和不同大小的锚框，并通过预测锚框的类别和偏移量（即预测边界框）检测不同大小的目标，因此单发多框检测是一个多尺度的目标检测模型。\n",
    "\n",
    "![单发多框检测模型主要由一个基础网络块和若干多尺度特征块串联而成](../img/ssd.svg)\n",
    "\n",
    "\n",
    "接下来我们介绍如何实现图中的各个模块。我们先介绍如何实现类别预测和边界框预测。\n",
    "\n",
    "### 类别预测层\n",
    "\n",
    "设目标的类别个数为$q$。每个锚框的类别个数将是$q+1$，其中类别0表示锚框只包含背景。在某个尺度下，设特征图的高和宽分别为$h$和$w$，如果以其中每个单元为中心生成$a$个锚框，那么我们需要对$hwa$个锚框进行分类。如果使用全连接层作为输出，很容易导致模型参数过多。回忆[“网络中的网络（NiN）”](../chapter_convolutional-neural-networks/nin.ipynb)一节介绍的使用卷积层的通道来输出类别预测的方法。单发多框检测采用同样的方法来降低模型复杂度。\n",
    "\n",
    "具体来说，类别预测层使用一个保持输入高和宽的卷积层。这样一来，输出和输入在特征图宽和高上的空间坐标一一对应。考虑输出和输入同一空间坐标$(x,y)$：输出特征图上$(x,y)$坐标的通道里包含了以输入特征图$(x,y)$坐标为中心生成的所有锚框的类别预测。因此输出通道数为$a(q+1)$，其中索引为$i(q+1) + j$（$0 \\leq j \\leq q$）的通道代表了索引为$i$的锚框有关类别索引为$j$的预测。\n",
    "\n",
    "下面我们定义一个这样的类别预测层：指定参数$a$和$q$后，它使用一个填充为1的$3\\times3$卷积层。该卷积层的输入和输出的高和宽保持不变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "from mxnet import autograd, contrib, gluon, image, init, nd\n",
    "from mxnet.gluon import loss as gloss, nn\n",
    "import time\n",
    "\n",
    "def cls_predictor(num_anchors, num_classes):\n",
    "    return nn.Conv2D(num_anchors * (num_classes + 1), kernel_size=3,\n",
    "                     padding=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边界框预测层\n",
    "\n",
    "边界框预测层的设计与类别预测层的设计类似。唯一不同的是，这里需要为每个锚框预测4个偏移量，而不是$q+1$个类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    }
   },
   "outputs": [],
   "source": [
    "def bbox_predictor(num_anchors):\n",
    "    return nn.Conv2D(num_anchors * 4, kernel_size=3, padding=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 连结多尺度的预测\n",
    "\n",
    "前面提到，单发多框检测根据多个尺度下的特征图生成锚框并预测类别和偏移量。由于每个尺度上特征图的形状或以同一单元为中心生成的锚框个数都可能不同，因此不同尺度的预测输出形状可能不同。\n",
    "\n",
    "在下面的例子中，我们对同一批量数据构造两个不同尺度下的特征图`Y1`和`Y2`，其中`Y2`相对于`Y1`来说高和宽分别减半。以类别预测为例，假设以`Y1`和`Y2`特征图中每个单元生成的锚框个数分别是5和3，当目标类别个数为10时，类别预测输出的通道数分别为$5\\times(10+1)=55$和$3\\times(10+1)=33$。预测输出的格式为(批量大小, 通道数, 高, 宽)。可以看到，除了批量大小外，其他维度大小均不一样。我们需要将它们变形成统一的格式并将多尺度的预测连结，从而让后续计算更简单。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 55, 20, 20), (2, 33, 10, 10))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def forward(x, block):\n",
    "    block.initialize()\n",
    "    return block(x)\n",
    "\n",
    "Y1 = forward(nd.zeros((2, 8, 20, 20)), cls_predictor(5, 10))\n",
    "Y2 = forward(nd.zeros((2, 16, 10, 10)), cls_predictor(3, 10))\n",
    "(Y1.shape, Y2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通道维包含中心相同的锚框的预测结果。我们首先将通道维移到最后一维。因为不同尺度下批量大小仍保持不变，我们可以将预测结果转成二维的(批量大小, 高$\\times$宽$\\times$通道数)的格式，以方便之后在维度1上的连结。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [],
   "source": [
    "def flatten_pred(pred):\n",
    "    return pred.transpose((0, 2, 3, 1)).flatten()\n",
    "\n",
    "def concat_preds(preds):\n",
    "    return nd.concat(*[flatten_pred(p) for p in preds], dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样一来，尽管`Y1`和`Y2`形状不同，我们仍然可以将这两个同一批量不同尺度的预测结果连结在一起。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 25300)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_preds([Y1, Y2]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高和宽减半块\n",
    "\n",
    "为了在多尺度检测目标，下面定义高和宽减半块`down_sample_blk`。它串联了两个填充为1的$3\\times3$卷积层和步幅为2的$2\\times2$最大池化层。我们知道，填充为1的$3\\times3$卷积层不改变特征图的形状，而后面的池化层则直接将特征图的高和宽减半。由于$1\\times 2+(3-1)+(3-1)=6$，输出特征图中每个单元在输入特征图上的感受野形状为$6\\times6$。可以看出，高和宽减半块使输出特征图中每个单元的感受野变得更广阔。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [],
   "source": [
    "def down_sample_blk(num_channels):\n",
    "    blk = nn.Sequential()\n",
    "    for _ in range(2):\n",
    "        blk.add(nn.Conv2D(num_channels, kernel_size=3, padding=1),\n",
    "                nn.BatchNorm(in_channels=num_channels),\n",
    "                nn.Activation('relu'))\n",
    "    blk.add(nn.MaxPool2D(2))\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试高和宽减半块的前向计算。可以看到，它改变了输入的通道数，并将高和宽减半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 10, 10, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forward(nd.zeros((2, 3, 20, 20)), down_sample_blk(10)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础网络块\n",
    "\n",
    "基础网络块用来从原始图像中抽取特征。为了计算简洁，我们在这里构造一个小的基础网络。该网络串联3个高和宽减半块，并逐步将通道数翻倍。当输入的原始图像的形状为$256\\times256$时，基础网络块输出的特征图的形状为$32 \\times 32$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 64, 32, 32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def base_net():\n",
    "    blk = nn.Sequential()\n",
    "    for num_filters in [16, 32, 64]:\n",
    "        blk.add(down_sample_blk(num_filters))\n",
    "    return blk\n",
    "\n",
    "forward(nd.zeros((2, 3, 256, 256)), base_net()).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 完整的模型\n",
    "\n",
    "单发多框检测模型一共包含5个模块，每个模块输出的特征图既用来生成锚框，又用来预测这些锚框的类别和偏移量。第一模块为基础网络块，第二模块至第四模块为高和宽减半块，第五模块使用全局最大池化层将高和宽降到1。因此第二模块至第五模块均为图9.4中的多尺度特征块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "10"
    }
   },
   "outputs": [],
   "source": [
    "def get_blk(i):\n",
    "    if i == 0:\n",
    "        blk = base_net()\n",
    "    elif i == 4:\n",
    "        blk = nn.GlobalMaxPool2D()\n",
    "    else:\n",
    "        blk = down_sample_blk(128)\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们定义每个模块如何进行前向计算。跟之前介绍的卷积神经网络不同，这里不仅返回卷积计算输出的特征图`Y`，还返回根据`Y`生成的当前尺度的锚框，以及基于`Y`预测的锚框类别和偏移量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "11"
    }
   },
   "outputs": [],
   "source": [
    "def blk_forward(X, blk, size, ratio, cls_predictor, bbox_predictor):\n",
    "    Y = blk(X)\n",
    "    anchors = contrib.ndarray.MultiBoxPrior(Y, sizes=size, ratios=ratio)\n",
    "    cls_preds = cls_predictor(Y)\n",
    "    bbox_preds = bbox_predictor(Y)\n",
    "    return (Y, anchors, cls_preds, bbox_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们提到，图9.4中较靠近顶部的多尺度特征块用来检测尺寸较大的目标，因此需要生成较大的锚框。我们在这里先将0.2到1.05之间均分5份，以确定不同尺度下锚框大小的较小值0.2、0.37、0.54等，再按$\\sqrt{0.2 \\times 0.37} = 0.272$、$\\sqrt{0.37 \\times 0.54} = 0.447$等来确定不同尺度下锚框大小的较大值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    }
   },
   "outputs": [],
   "source": [
    "sizes = [[0.2, 0.272], [0.37, 0.447], [0.54, 0.619], [0.71, 0.79],\n",
    "         [0.88, 0.961]]\n",
    "ratios = [[1, 2, 0.5]] * 5\n",
    "num_anchors = len(sizes[0]) + len(ratios[0]) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们就可以定义出完整的模型`TinySSD`了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    }
   },
   "outputs": [],
   "source": [
    "class TinySSD(nn.Block):\n",
    "    def __init__(self, num_classes, **kwargs):\n",
    "        super(TinySSD, self).__init__(**kwargs)\n",
    "        self.num_classes = num_classes\n",
    "        for i in range(5):\n",
    "            # 即赋值语句self.blk_i = get_blk(i)\n",
    "            setattr(self, 'blk_%d' % i, get_blk(i))\n",
    "            setattr(self, 'cls_%d' % i, cls_predictor(num_anchors,\n",
    "                                                      num_classes))\n",
    "            setattr(self, 'bbox_%d' % i, bbox_predictor(num_anchors))\n",
    "\n",
    "    def forward(self, X):\n",
    "        anchors, cls_preds, bbox_preds = [None] * 5, [None] * 5, [None] * 5\n",
    "        for i in range(5):\n",
    "            # getattr(self, 'blk_%d' % i)即访问self.blk_i\n",
    "            X, anchors[i], cls_preds[i], bbox_preds[i] = blk_forward(\n",
    "                X, getattr(self, 'blk_%d' % i), sizes[i], ratios[i],\n",
    "                getattr(self, 'cls_%d' % i), getattr(self, 'bbox_%d' % i))\n",
    "        # reshape函数中的0表示保持批量大小不变\n",
    "        return (nd.concat(*anchors, dim=1),\n",
    "                concat_preds(cls_preds).reshape(\n",
    "                    (0, -1, self.num_classes + 1)), concat_preds(bbox_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们创建单发多框检测模型实例并对一个高和宽均为256像素的小批量图像`X`做前向计算。我们在之前验证过，第一模块输出的特征图的形状为$32 \\times 32$。由于第二至第四模块为高和宽减半块、第五模块为全局池化层，并且以特征图每个单元为中心生成4个锚框，每个图像在5个尺度下生成的锚框总数为$(32^2 + 16^2 + 8^2 + 4^2 + 1)\\times 4 = 5444$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output anchors: (1, 5444, 4)\n",
      "output class preds: (32, 5444, 2)\n",
      "output bbox preds: (32, 21776)\n"
     ]
    }
   ],
   "source": [
    "net = TinySSD(num_classes=1)\n",
    "net.initialize()\n",
    "X = nd.zeros((32, 3, 256, 256))\n",
    "anchors, cls_preds, bbox_preds = net(X)\n",
    "\n",
    "print('output anchors:', anchors.shape)\n",
    "print('output class preds:', cls_preds.shape)\n",
    "print('output bbox preds:', bbox_preds.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "\n",
    "下面我们描述如何一步步训练单发多框检测模型来进行目标检测。\n",
    "\n",
    "### 读取数据集和初始化\n",
    "\n",
    "我们读取[“目标检测数据集（皮卡丘）”](object-detection-dataset.ipynb)一节构造的皮卡丘数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "14"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "train_iter, _ = d2l.load_data_pikachu(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在皮卡丘数据集中，目标的类别数为1。定义好模型以后，我们需要初始化模型参数并定义优化算法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "15"
    }
   },
   "outputs": [],
   "source": [
    "ctx, net = d2l.try_gpu(), TinySSD(num_classes=1)\n",
    "net.initialize(init=init.Xavier(), ctx=ctx)\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd',\n",
    "                        {'learning_rate': 0.2, 'wd': 5e-4})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义损失函数和评价函数\n",
    "\n",
    "目标检测有两个损失：一是有关锚框类别的损失，我们可以重用之前图像分类问题里一直使用的交叉熵损失函数；二是有关正类锚框偏移量的损失。预测偏移量是一个回归问题，但这里不使用前面介绍过的平方损失，而使用$L_1$范数损失，即预测值与真实值之间差的绝对值。掩码变量`bbox_masks`令负类锚框和填充锚框不参与损失的计算。最后，我们将有关锚框类别和偏移量的损失相加得到模型的最终损失函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "16"
    }
   },
   "outputs": [],
   "source": [
    "cls_loss = gloss.SoftmaxCrossEntropyLoss()\n",
    "bbox_loss = gloss.L1Loss()\n",
    "\n",
    "def calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks):\n",
    "    cls = cls_loss(cls_preds, cls_labels)\n",
    "    bbox = bbox_loss(bbox_preds * bbox_masks, bbox_labels * bbox_masks)\n",
    "    return cls + bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以沿用准确率评价分类结果。因为使用了$L_1$范数损失，我们用平均绝对误差评价边界框的预测结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "18"
    }
   },
   "outputs": [],
   "source": [
    "def cls_eval(cls_preds, cls_labels):\n",
    "    # 由于类别预测结果放在最后一维，argmax需要指定最后一维\n",
    "    return (cls_preds.argmax(axis=-1) == cls_labels).sum().asscalar()\n",
    "\n",
    "def bbox_eval(bbox_preds, bbox_labels, bbox_masks):\n",
    "    return ((bbox_labels - bbox_preds) * bbox_masks).abs().sum().asscalar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "\n",
    "在训练模型时，我们需要在模型的前向计算过程中生成多尺度的锚框`anchors`，并为每个锚框预测类别`cls_preds`和偏移量`bbox_preds`。之后，我们根据标签信息`Y`为生成的每个锚框标注类别`cls_labels`和偏移量`bbox_labels`。最后，我们根据类别和偏移量的预测和标注值计算损失函数。为了代码简洁，这里没有评价测试数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "19"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  5, class err 2.88e-03, bbox mae 3.21e-03, time 7.7 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 10, class err 2.61e-03, bbox mae 2.88e-03, time 7.7 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 15, class err 2.66e-03, bbox mae 2.77e-03, time 7.7 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, class err 2.34e-03, bbox mae 2.50e-03, time 7.8 sec\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(20):\n",
    "    acc_sum, mae_sum, n, m = 0.0, 0.0, 0, 0\n",
    "    train_iter.reset()  # 从头读取数据\n",
    "    start = time.time()\n",
    "    for batch in train_iter:\n",
    "        X = batch.data[0].as_in_context(ctx)\n",
    "        Y = batch.label[0].as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            # 生成多尺度的锚框，为每个锚框预测类别和偏移量\n",
    "            anchors, cls_preds, bbox_preds = net(X)\n",
    "            # 为每个锚框标注类别和偏移量\n",
    "            bbox_labels, bbox_masks, cls_labels = contrib.nd.MultiBoxTarget(\n",
    "                anchors, Y, cls_preds.transpose((0, 2, 1)))\n",
    "            # 根据类别和偏移量的预测和标注值计算损失函数\n",
    "            l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
    "                          bbox_masks)\n",
    "        l.backward()\n",
    "        trainer.step(batch_size)\n",
    "        acc_sum += cls_eval(cls_preds, cls_labels)\n",
    "        n += cls_labels.size\n",
    "        mae_sum += bbox_eval(bbox_preds, bbox_labels, bbox_masks)\n",
    "        m += bbox_labels.size\n",
    "\n",
    "    if (epoch + 1) % 5 == 0:\n",
    "        print('epoch %2d, class err %.2e, bbox mae %.2e, time %.1f sec' % (\n",
    "            epoch + 1, 1 - acc_sum / n, mae_sum / m, time.time() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测\n",
    "\n",
    "在预测阶段，我们希望能把图像里面所有我们感兴趣的目标检测出来。下面读取测试图像，将其变换尺寸，然后转成卷积层需要的四维格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "20"
    }
   },
   "outputs": [],
   "source": [
    "img = image.imread('../img/pikachu.jpg')\n",
    "feature = image.imresize(img, 256, 256).astype('float32')\n",
    "X = feature.transpose((2, 0, 1)).expand_dims(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们通过`MultiBoxDetection`函数根据锚框及其预测偏移量得到预测边界框，并通过非极大值抑制移除相似的预测边界框。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "21"
    }
   },
   "outputs": [],
   "source": [
    "def predict(X):\n",
    "    anchors, cls_preds, bbox_preds = net(X.as_in_context(ctx))\n",
    "    cls_probs = cls_preds.softmax().transpose((0, 2, 1))\n",
    "    output = contrib.nd.MultiBoxDetection(cls_probs, bbox_preds, anchors)\n",
    "    idx = [i for i, row in enumerate(output[0]) if row[0].asscalar() != -1]\n",
    "    return output[0, idx]\n",
    "\n",
    "output = predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们将置信度不低于0.3的边界框筛选为最终输出用以展示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "22"
    }
   },
   "outputs": [
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    "d2l.set_figsize((5, 5))\n",
    "\n",
    "def display(img, output, threshold):\n",
    "    fig = d2l.plt.imshow(img.asnumpy())\n",
    "    for row in output:\n",
    "        score = row[1].asscalar()\n",
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    "            continue\n",
    "        h, w = img.shape[0:2]\n",
    "        bbox = [row[2:6] * nd.array((w, h, w, h), ctx=row.context)]\n",
    "        d2l.show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')\n",
    "\n",
    "display(img, output, threshold=0.3)"
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   "source": [
    "## 小结\n",
    "\n",
    "* 单发多框检测是一个多尺度的目标检测模型。该模型基于基础网络块和各个多尺度特征块生成不同数量和不同大小的锚框，并通过预测锚框的类别和偏移量检测不同大小的目标。\n",
    "* 单发多框检测在训练中根据类别和偏移量的预测和标注值计算损失函数。\n",
    "\n",
    "\n",
    "\n",
    "## 练习\n",
    "\n",
    "* 限于篇幅原因，实验中忽略了单发多框检测的一些实现细节。你能从以下几个方面进一步改进模型吗？\n",
    "\n",
    "\n",
    "### 损失函数\n",
    "\n",
    "将预测偏移量用到的$L_1$范数损失替换为平滑$L_1$范数损失。它在零点附近使用平方函数从而更加平滑，这是通过一个超参数$\\sigma$来控制平滑区域的：\n",
    "\n",
    "$$\n",
    "f(x) =\n",
    "    \\begin{cases}\n",
    "    (\\sigma x)^2/2,& \\text{if }|x| < 1/\\sigma^2\\\\\n",
    "    |x|-0.5/\\sigma^2,& \\text{otherwise}\n",
    "    \\end{cases}\n",
    "$$\n",
    "\n",
    "当$\\sigma$很大时该损失类似于$L_1$范数损失。当它较小时，损失函数较平滑。"
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    "sigmas = [10, 1, 0.5]\n",
    "lines = ['-', '--', '-.']\n",
    "x = nd.arange(-2, 2, 0.1)\n",
    "d2l.set_figsize()\n",
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    "for l, s in zip(lines, sigmas):\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在类别预测时，实验中使用了交叉熵损失：设真实类别$j$的预测概率是$p_j$，交叉熵损失为$-\\log p_j$。我们还可以使用焦点损失（focal loss）[2]：给定正的超参数$\\gamma$和$\\alpha$，该损失的定义为\n",
    "\n",
    "$$ - \\alpha (1-p_j)^{\\gamma} \\log p_j.$$\n",
    "\n",
    "可以看到，增大$\\gamma$可以有效减小正类预测概率较大时的损失。"
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      "text/plain": [
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     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def focal_loss(gamma, x):\n",
    "    return -(1 - x) ** gamma * x.log()\n",
    "\n",
    "x = nd.arange(0.01, 1, 0.01)\n",
    "for l, gamma in zip(lines, [0, 1, 5]):\n",
    "    y = d2l.plt.plot(x.asnumpy(), focal_loss(gamma, x).asnumpy(), l,\n",
    "                     label='gamma=%.1f' % gamma)\n",
    "d2l.plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和预测\n",
    "\n",
    "* 当目标在图像中占比较小时，模型通常会采用比较大的输入图像尺寸。\n",
    "* 为锚框标注类别时，通常会产生大量的负类锚框。可以对负类锚框进行采样，从而使数据类别更加平衡。这个可以通过设置`MultiBoxTarget`函数的`negative_mining_ratio`参数来完成。\n",
    "* 在损失函数中为有关锚框类别和有关正类锚框偏移量的损失分别赋予不同的权重超参数。\n",
    "* 参考单发多框检测论文，还有哪些方法可以评价目标检测模型的精度 [1]？\n",
    "\n",
    "\n",
    "\n",
    "## 参考文献\n",
    "\n",
    "[1] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016, October). Ssd: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.\n",
    "\n",
    "[2] Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2018). Focal loss for dense object detection. IEEE transactions on pattern analysis and machine intelligence.\n",
    "\n",
    "\n",
    "## 扫码直达[讨论区](https://discuss.gluon.ai/t/topic/2511)\n",
    "\n",
    "![](../img/qr_ssd.svg)"
   ]
  }
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